Scientists learning to program “synthetic life” with DNA

Three experts at AAAS Chicago 2009 detail recent advances in “synthetic life …

Much of the success of modern personal computers can be boiled down to a few basic concepts: modularity, standardization, and off-the-shelf components. Hard drives fit into standard-sized slots, connect to motherboards via nearly ubiquitous, industry-standard connections, etc. Now, genetic engineers and synthetic biologists are using those same principles to foster discoveries and hurdle engineering obstacles in their fields.

Three scientists detailed recent advances in “synthetic life” at the American Association for the Advancement of Science meeting on Friday. This is the field that many hope will bring you wonder drugs and super materials all made in microbial factories. To get there, researchers have closely following the road paved by computer scientists decades ago, giving cellular functions logical operators and abstracting parts of the development process from the (chemical) hardware. Fortunately for us, the scientists pushing the boundaries of synthetic life are not hoping to recreate Frankenstein’s monster, or even its single-celled equivalent. Right now, they are more interested in using the machinery of the cell—DNA, RNA, and other molecular machinery—to manufacture compounds otherwise unavailable en masse.

Programming with RNA

Christina Smolke, assistant professor of bioengineering at Stanford and the session organizer, introduced us to programming in RNA, the chemical used to translate the genetic code into proteins. To Smokle, RNA is “inherently programmable.” The two-dimensional folds of each molecule give researchers a strong hint of the functions it can serve, and those folds can be predicted with the help of advanced simulations. Much of the design work, then, can be done on the computer before anyone has to snap on a pair of latex gloves. These features have made it an appealing tool for biological computing work.

Smolke’s basic RNA programming framework involves three components, all connected by nodes that are the genetic equivalent of standard computer connections like USB and SATA. To “compile” these RNA programs, Smolke strings together a series of sensors, transmitters, and actuators. The first module, a sensor, waits for the presence of a particular chemical before passing the signal on to a transmitter. The transmitter then relays the message to an actuator, which, for example, can turn on the coding region of another RNA strand, leading to the production of a particular protein.

To “compile” these RNA programs, Smolke strings together a series of sensors, transmitters, and actuators. The first module, a sensor, waits for the presence of a particular chemical before passing the signal on to a transmitter. The transmitter then relays the message to an actuator, which, for example, can turn on the coding region of another RNA strand, leading to the production of a particular protein.

Assembling an RNA program with different numbers of these three different modules allows Smolke and other synthetic biologists to build logic into their RNA code. Sound familiar? To explore the idea, let’s look at a theorized cancer treatment that uses reprogrammed cells from our immune system as an example. An oncologist in the future may prescribe an injection of special t-cells that are designed to attack tumor cells. If these programmed cells ran roughshod throughout the patient’s body, they could also harm healthy cells. So scientists hope to give clinicians control over the cells with a series of logical gates. By adding a series of AND gates, scientists can ensure the anti-tumor t-cells will lay dormant until they sense a certain chemical cue—say by an injection administered by a nurse. In the presence of the chemical cue, the cell perks up and can begin their work on the tumor.

Debugging the genome

Logic and code weren’t enough for Jay Keasling, professor of bioengineering at the University of California, Berkeley, who is on a quest to manufacture a low-cost equivalent of artemisinin, currently the best anti-malaria drug in use. Purified from the Artemisia annua plant, the drug is too rare and expensive for many of the people who need it, which has prodded researchers like Keasling to action.

Keasling and his lab use yeast and E. coli in their experiments, adding and subtracting genes from the organisms’ genomes to produce the basic compounds needed to synthesize artemisinin. Like many ambitious research projects, they ran into a few snags. To smooth the kinks, Keasling created biological debugging routines. When their cells refused to grow and divide, for example, he and his lab yanked out one chemical pathway after another in search of the problem.

While the process of developing these routines was time consuming, their efforts paid dividends. “The debugging routines are good once you’ve got them,” Keasling said. Keasling’s students and staff now rapidly identify problems in the code, slashing the amount of time they spend rooting around for misbehaving strands of DNA.

What's next?

With synthetic biologists programming in DNA and RNA and debugging their genetic code, what’s left? Since they’ve followed the computing paradigm this far, Drew Endy, assistant professor of bioengineering at Stanford, doesn’t want to see it end. Endy, like many others, thinks abstraction helped foster the success and pervasiveness of computers. Programming languages can be abstracted from hardware interfaces, and Endy wants researchers to be able to design synthetic life systems without having to hand-code the DNA. He wants to make sure that each level, from DNA synthesis to device implementation, can be abstracted from the others. His BioBricks database is a step in that direction, providing experts an open-access catalog of standardized biological components.

Endy closed the session with some forward-looking comments. In addition to cribbing the programming and abstraction paradigms from computing, he would like to see the field advance rapidly, following a biologist’s equivalent of Moore’s Law. There’s some evidence of this already. DNA synthesis is already moving faster than Moore’s Law, with productivity doubling every 14 months. He also pointed to a site called Ars Synthetica (we’re flattered) as evidence of the field’s push toward the mainstream—it provides a forum to discuss the pros, cons, and thorny ethical issues of synthetic biology. And it's a good thing, too. “Synthetic biology” sounds too much like monsters and doomsday scenarios, not anti-malaria medications and cellular computers.

In any case, synthetic biology sounds like it’s here to stay. The potential pharmaceutical benefits are enormous, and special biomaterials like spider silk may be the next big thing. At some point, we’re likely to need trillions of microbial factories to bring those fancy goods to market.

Smolke’s basic RNA programming framework involves three components, all connected by nodes that are the genetic equivalent of standard computer connections like USB and SATA. To “compile” these RNA programs, Smolke strings together a series of sensors, transmitters, and actuators.

I have to call out the fact that when I write software, I don't hook my classes together using USB or SATA. :P

I can only assume this is analogous to having standardized connectors and customized hardware, such that no software is required and everything is run through (essentially) genetic FPGAs.

It sounds like their long-term goal, however, is to make genetic programming as easy as LISP.

I'm wondering how similar this programing of DNA/RNA is to what our own cells do. It sounds like a much simpler version of Hox genes and very similar to what the proteins to what the proteins responsible for Hg resistance do, except in that case the sensor, transmitter and actuator are all one protein thats sitting on the DNA.

Originally posted by Rochefort:Very interesting work, but I think you've strained the computational metaphor in applying it here. For instance, I don't think that "compilation" is a very good metaphor for what Smolke is doing.

I think the computer metaphor has been stretched way too far in this article, but I do see how writing it in this manner would make the content more accessible to a tech crowd.

I'm a first year grad student doing research in synthetic biology, and although I'm not very well-read yet, I have never encountered literature where they use a computer metaphor to describe synthetic biology. I don't doubt that some people use such a metaphor, though.

quote:

Originally posted by Specter64:I'm wondering how similar this programing of DNA/RNA is to what our own cells do. It sounds like a much simpler version of Hox genes and very similar to what the proteins to what the proteins responsible for Hg resistance do, except in that case the sensor, transmitter and actuator are all one protein thats sitting on the DNA.

Honestly, the article does a pretty poor job of describing exactly what synthetic biology is. Synthetic biology can be different things depending who you ask, but as it is described in the article, it's just the utilization of various genetic components such as transcription factors to make genetic 'circuits.'

For example, the quorum-sensing system found in most species of bacteria is comprised of 2 genetic components: an autoinducer synthase protein, which produces an autoinducer molecule, and a transcriptional regulator protein which is activated in the presence of a high enough concentration of this autoinducer molecule. The autoinducer molecules freely diffuses across cell membranes and when the population density of cells is high enough, the concentration of autoinducer molecule crosses a threshold and activates the transcriptional activator proteins. These transcriptional activators then bind to certain sequences of DNA it recognizes and activates transcription of those genes.

Transcriptional activation in the presence of certain molecules (such as hormones) is extremely common in nature. Synthetic biology is merely the customization of genetic regulation achieve a specific goal i.e. the transcription of a desired gene.

Originally posted by DyDx:I think the computer metaphor has been stretched way too far in this article, but I do see how writing it in this manner would make the content more accessible to a tech crowd.

FWIW, Smolke and the other synthetic biologists at the talk relied heavily on the computing metaphor, right down to processor gates and debugging. While it may not be a perfect analogy (and what is?), I think it doesn't do a bad job describing what can be done with synthetic biology. Not necessarily all that can be done, but a peak into the possibilities.

I wouldn't know if it has been mentioned on Ars Technica before, but MIT maintains a sort of catalogue of Biological parts, relying on eletronics and computer metaphors to classify them - sensors, activators , regulators etc. Take a look at the Registry of Standard Biological parts